FIG. 1 illustrates an example cellular wireless communications system 2. A plurality of mobile stations (MSs) 10 (sometimes called mobile terminals, user equipments, etc.) are located in a geographical service area covered by cells C1 through C6. Radio base stations (RBSs) 4 are positioned within the geographic area covered by the cells C1 through C6 and act as an interface between the mobile station 10 and the wireless communications system 2. The radio base stations 4 are typically connected to a base station controller (BSC) 6 or radio network controller (RNC), which in turn is connected to one or more core network nodes like a mobile switching center (MSC), a serving GSM support node (SGSN), etc. The BSC 6 may be connected to other BSCs, and the core network node(s) are usually coupled to external networks like a public switched telephone network (PSTN) 8 and/or the Internet.
The wireless communications system 2 in FIG. 1 has only a limited frequency band it is allowed to use. Each cell is assigned a set of channels in the allowed frequency band. Each set of channels is reused after separation of a certain number of cells so that adjacent cells are assigned a different set of channels to reduce/alleviate co-channel interference. Thus, for wireless communications systems that divide the spectrum into narrow frequency bands, such as GSM, careful frequency planning is usually required.
Despite the best efforts of network planners, some degree of adjacent channel interference (ACI) is unavoidable. The energy of two signals on adjacent carrier frequencies will often result in undesired interference because the bandwidth of the spectrum of the signals is larger than the separation between the carriers. An example of adjacent channel interference is shown in the graph of FIG. 2. The upper frequency portion of channel 1 spills over into the lower frequency portion of channel 2 interfering with the desired signals on channel 1. A consequence of such adjacent interference is loss of spectral efficiency. Users may experience dropped calls, lower data rates, or other disruptions, and operators may experience revenue loss due to loss of capacity and lower customer satisfaction.
One way to suppress ACI is with preset bandpass or lowpass filters (analog or digital). Although such preset filters generally work well to suppress ACI when it is present, the price for using them is a substantial loss in receiver sensitivity. In another words, the filters remove some of the desired signal as well as the ACI. More specifically, such filters “clip” the desired signal energy so that the ability to suppress adjacent interference is increased, but at the price of a decreased capability to detect and decode weak signals in the presence of thermal noise. Because it operates regardless of ACI, a preset ACI filters/attenuates some of the frequency spectrum of the desired signal even when no ACI is present. Another problem is that co-channel interference suppression is diminished with narrow bandpass/lowpass filtering.
As systems have evolved, e.g., EDGE was introduced to GSM, new modulation techniques have been employed. For the EDGE example, 8PSK modulation was introduced. The 8PSK modulation required wider filters then the previous modulation used because 8PSK-modulated signals are very sensitive to Inter-Symbol-Interference (ISI). Narrow bandpass or lowpass filters increase ISI. The wider filters used in EDGE also improve receiver sensitivity but at the cost of less effective adjacent interference suppression.
A response to this dilemma is to use an adaptive ACI suppression algorithm that analyzes the received signal, searches for interference of any sort, and if some interference is found, then attempts to suppress it. One type of adaptive ACI suppression algorithm is based on statistical models where the estimated interference is “fit” into a given mathematical model or family of models. Adaptive ACI suppression algorithms are based on an analysis of training symbols and possibly payload data in order to produce estimates of the spectrum or autocorrelation of the interference. These interference estimates are then used to design adaptive ACI suppression filters typically implemented in software. An advantage of this approach is that if no adjacent channel interference is present, then no ACI filtering is done. Consequently, the sensitivity of the receiver is not degraded.
A problem with the adaptive ACI suppression approach is that such software-based interference suppression filters are typically suboptimal. First, the number of samples available to perform the estimates is often very small. Second, the radio environment is extremely hostile, leading to great variations in the desired signal and the interference, even during the duration of a single transmission burst. Third, for reasons of computational load, the interference must be approximated using relatively simple models, even though its spectrum can be quite complicated, e.g., when there are several simultaneous interferers. All three result in relatively inaccurate interference estimates, and hence, suboptimal interference suppression filters. Other adaptive algorithms require the filtering of the incoming signal and making some comparison between estimates of the quality of the original and filtered signals. These algorithms are ad-hoc, are difficult (or in practice impossible) to analyze theoretically, must be tuned based solely on simulations, and often do not have good performance.
It is an object to provide technology that improves the suppression of adjacent channel interference (ACI) in light of the problems noted above.